Participatory mHealth - microsoft.com€¦ · – User engagement/experience: motivate sustainable...

Smartphones are proximate, pervasive, programmable, personal; offer data/interaction in real time, real place, real context

Participatory mHealth

Deborah EstrinUCLA Center for Embedded Networked Sensing (CENS)

and OpenmHealth.org (~fall 2011). contact: [email protected]

in collaboration with faculty, students, staff at CENS, UCLA, UCSF...

Stovepipe Architecture

Mobile platformsiPhone/Android /Feature Phones

Patient /Caregivers Patient /Caregivers

Open mHealth Architecture


Re-usable health data and knowledge services

Standardized personal data vaults and health specific data exchange protocols

Analysis /visualization/

feedback

Processing

Storage

Data transport

Data capture

AP

PL

IC

AT

IO

NS

1Thursday, July 14, 2011

mailto:[email protected]

mailto:[email protected]

experience sampling streams

personal data repository

event detection

processing

aggregate measures, trends, patterns

our self report

visualization

our actions

Photo: Marshall Astor, WWW

context and activity traces

mHealth

Use mobile devices to enhance health and wellness by extending health interventions and research beyond the reach of traditional clinical care.


motivation3 behaviors (diet, exercise, smoking) cause 1/3rd of US deaths

50% Americans have 1 or more chronic diseases

age of onset getting younger

chronic disease prevention/management/research happens in the context of daily life, outside of clinical setting

approachsupport individuals, communities, clinicians to continuously improve patient-centered, personalized, health and healthcare.

mobile devices offer proximity, pervasiveness, programmability, personalization, affordability.

Why participatory mHealth ?


Dashboard access

Internet

Data collec0on

Data analysis, privacy preserving storage

Ramanathan, Selsky, et al

built to explore participatory mHealth


Phonetop Bu:ons

Internet

GPS ACCELEROMETER

Ac0graphy

Loca0on and 0me triggered

surveys

Automated capture

Data collec0on



Participatory design: functionality shaped by focus groups, interviews

6Ramanathan et al

AndWellnessApplicationServer

<survey> <title>Alcohol</title> <prompt> <text>How many drinks did you have today?</text> </prompt></survey>

Survey Authoring

Image Capture

Stress, Eating Buttons

GPS ACCELEROMETER

Smart Classifier

Actigraphy

Survey Input

Trigger Authoringtrigger logic as function of

location, time, activity, prompt responses

>100 (somewhat) diverse participants: young moms, young men living with HIV, immigrant women, breast cancer survivors, and recruited UCLA student testers


Focus groups summary (n=72)

Population Primary application features discussed

Young Momsn=23

Engaging participation without increasing user burden•Customization of reminder times/locations for convenience•Image capture of food to increase accountability•Set, manage, and monitor progress towards a goal•Light-weight data capture•Primary interaction to take place on the phone

Immigrant Womenn=20

Mechanisms to help people achieve a goal•Set, manage, and monitor progress towards a goal•Helpful tips and problem solving (suggested by phone)

People Living with HIV

n=29

1) Privacy and security of data•Password protection on phone a must•Nondescript text to hide the intent of sensitive questions•Location tracking is controversial, granular control a must•Data anonymization for sharing with counselor, coach, medical provider

2) Customizatin of Reminders • Medication adherence reminders, especially using location• Safe sex reminders

Ramanathan, Swendeman, Dawson, Estrin, Rotheram-Borus7Thursday, July 14, 2011

Notable feature requests

• Images: Moms LOVED this feature for food, SA women did not.

• Triggers: Control of timing important to all--need trigger authoring and personalization

• Buttons: Most moms willing to answer at least briefly ‘in the moment’, while SA women almost all wanted to answer only at the end of the day.

• Feedback: Very few interested in seeing simple quantifications of their responses. Helpful tips and motivational messaging most popular. SA explicitly preferred *against themselves* vs competitive feedback with group.

• Server vs Phone: Very few willing/interested to access server. Most wanted interaction solely on phone.

Ramanathan, Swendeman, et al


End-‐User Dashboards

Correla0ons in 0me and space

Internet

Work in progress for future release: time delayed correlations and correlations across behaviors


Ac0graphy over space Ac0graphy over 0me


Feedback

Configure

Chart Summaries

Table Summaries

Iyer, Ramanathan, et al

* To Be Released August, 2011


ubifit garden using on-body sensing, activity inference, and a personal, mobile display to encourage regular and varied physical activity

2 dub, u of washington

1 intel research seattle

had the garden

participants who…

did NOT have the garden

consolvo, mcdonald, landay, chi 09 consolvo et al, ubicomp 08 consolvo et al, chi 08 choudhury et al, ieee pervasive mag ‘08 froehlich et al, mobisys ‘07 consolvo, paulos, smith, mobile persuasion ’07

Of course...“feedback” should look more like this and be tailored to individual participants-- Ubifit (UW, Intel)


Researcher Dashboards

Survey Crea0on

Internet

Par0cipa0on Popula0on Sta0s0cs

* in progress for release, Dec, 2011

05

1015

2025

0.0 0.5 1.0 1.5 2.0 2.5 3.0Survey response rate

Num

ber o

f day

s



Admin Dashboards

Internet

User interac0on with phone

Remaining ba:ery on phone

Memory usage on phone

Filter by loca0on, 0me, user

Wu, Falaki, Estrin, Ramanathan13Thursday, July 14, 2011

andwellness.org

Java 6 + Tomcat 6 + MySQL 5.1

Internet

AndWellness system implementation

14

Server

JSON/HTTPS

JSON/HTTPS

Smartphone

Web Browser Clients

AndWellness

andwellness.org

Java 6 + Tomcat 7 + MySQL 5.1

RApache for datavis


What lies behind exposed API calls could be written in any programming language.

Server-side defined by HTTP APIs: GET and POST using JSON, standard HTTP data packaging


• Experience sampling, light-weight data capture (50%)

• Visualization/data presentation for end-user and researcher (15%)

• Smart triggers based on user configurable location, time, activity (25%)

• Background services for actigraphy, location tracing, system analytics (50%)

• Battery-preserving background services (50%)

• Open mHealth community building (15%)

• Participatory privacy policies and mechanisms (15%)

15

Status of AndWellness features to date(with guesstimate on how far along we are)

Ramanathan,, et al


Key research challenges

• Health sciences community:

– Establish validity and reliability of mHealth instruments

– Derive efficacy evidence base from rich usage, system analytics

– Behavior change: defining, implementing, and adapting interventions that support sustained and beneficial change across populations

• Technical community:

– Infovis: analysis, presentation, visualization, for self, clinician, researcher

– Resource management, efficiency (enable full-day phone operation with background activity and data capture)

– User modeling (eg community models (Choudhury) for activity classification, context, triggers

– User engagement/experience: motivate sustainable user participation with game mechanics, adaptive interfaces

– Selective sharing, usable privacy tools

– Open systems16

Ramanathan, et al16Thursday, July 14, 2011

Open mHealth System: functional components

EHR/PHR, social media

Data Exchange standards

Import and

Export

Dashboards,Presentation

Mobile Data Collection

Analysis Services

Configuration and

management

Personal dashboardsClinical

Community/social group dashboards

Reminders, Triggers

Prompted self-

reports

Automated measure-

ments (actigraphy, offboard de-

vices)

Feedback, social me-dia, goal setting

DataStore

Data Management

SecurePersonal

Data Vault

Core ID, Security Services

Data alignment

Time series summarization

Summary statistics

correlations

Trend and anomaly

identificationAggregateanalysis

InfoVis

External data layers

Game and guided-in-tervention

stats

Phone us-age patterns (communi-

cations, calendars,

alarms)Current

Stacklets

Activity classification

InfoVisextract and present relevant trends, patterns, anomalies, correlations across

diverse data streams and to diverse audiences

Needs: pre-processing, feature extraction, integration with machine learning libraries and statistical analysis tools, incorporation of external datasets, geo-spatial analyses, informative and configurable presentation


•Battery and resource monitor continuously guides applications to consume enough power to meet the deadline; trading off fidelity/resolution

Adaptive battery management for background applications

Falaki, et al

Energy Source

Interaction Metrics

Phone Resources

•Usage and context matter for battery management, e.g., 15% left battery at 10pm is not the same as 15% at 10am.

SystemSens

{GPS ACCELEROMETER

Andwellness

Energy Budget


19

Objectives• energy-efficiently sense semantic locations on battery-limited mobile devices• automatically learn and recognize semantic places and paths closer to user’s

interpretation of location• motivate user feedback to bridge between machine-learned and human-defined places

Selectively leverage GPS/Wi-Fi/Accelerometer when each is informative/efficient• people spend approximately 89% indoors, 5% in a vehicle, and 6% at outdoors on average

Signal patterns of surrounding RF beacons are

noisy but useful for detecting semantic places

Sensor traces from a single day following a normal routine

GPS position fixes are inaccurate most of the time but

informative when traveling paths connecting places

Many power saving opportunities exists

when the device is immobile

Semantic places and path


User modeling (sic) Using community similarity networksto handle population diversity (T. Choudhury, Cornell)

A

B

C

D

Data

Labels&

SimilaritySensitiveTraining

PhysicalSimilarityNetwork

LifestyleSimilarityNetwork

Sensor DataSimilarityNetwork

Similarity NetworkCo-Training

Community AModel

Community BModel

Community CModel

Community DModel


User engagement:informational incentives, feedback, game mechanics

Informational incentives, e.g., analytics about actions, encourage participation initially--See Consolvo, Choudhury, Mynatt

Micro-payments/rewards promoted even (sustained) participation in community data gathering--might also apply for participatory mHealth Reddy, et al 2009:• Micro-payments based on competition worked best for short bursty data collections• Very low baseline micro-payments discouraged individuals

Future directions: game mechanics, social media tie-ins, goal setting and monitoring tools, adaptive over time for sustainability, configurable

Mobile Ambient Wellbeing Display (T. Choudhury, Cornell)


Mobile App Personal Data Vault

Third Party Services

- Data Capture / Upload(Prompted, Automated)

- Reminders

- Feedback, Incentives

Well defined interfaces allowmobile functions to be mixed,matched, and shared

- User Identity and Authentication

- Long-term Data Management

Patient defined selective sharing with Open mHealth Server function

- Analytics for PersonalData Streams

- Interface to Clinical CarePlan, Personnel

- Integration with EHR/PHRs

- Cross Patient Aggregation

Well defined interfaces allowanalytics functions to be mixed,matched, shared, compared

Personal Data Vault (PDV): allow participants to retain control over their raw data by

decoupling capture and sharing

vault + filters = granular, assisted control over what/when you send to whom, what data says about you, whether you reveal who you are or share anonymously, ... M. Mun, et al, CONEXT 2010


But...can we make selective sharing usable?

PDV

1. Upload raw data

Content-Service Providers

3. Request data

4. Filter data to send

5. Send the filtered data

6. Check easy-to-read reports about continuous data sharing

Trace-audit

Step 6 and 7 can be repeated

7. Reconfigure ACLs

Rule Recommender

2. Configure privacy policies/ACLs

2. Configure privacy policies/ACLs

Granular ACLs

M. Mun, et al, CONEXT 2010

bounds, precision, frequency

across time, space,

Significant Location

Identification

Routine Place Selection

Spatial Bound Creation

Rule Recommender

Granular ACLs:


Importance of an open platform: avoid silos, promote innovation and transparency

Bootstrap rapid cycle of learning, sharing, deployment-~80 % (guesstimate) system components reusable-Largest missing pieces: authoring, analysis-visualization, feedback

Facilitate research in methodology, treatment-Systems gather usage data automatically for evaluation, iterative improvement-Encourage modularity and sharing in methodologies, practice

Development in the context of real applications and use-Collaborative/participatory design process with continual feedback from users-Diverse targeted pilots inform generalization, adaptation, expansion.

Explore balancing of privacy protection and data sharing-Variety of privacy/sharing policies-STransparency of research and data processes for participants


Open mHealth System: functional components

EHR/PHR, social media

Data Exchange standards

Import and

Export

Dashboards,Presentation

Mobile Data Collection

Analysis Services

Configuration and

management

Personal dashboardsClinical

Community/social group dashboards

Reminders, Triggers

Prompted self-

reports

Automated measure-

ments (actigraphy, offboard de-

vices)

Feedback, social me-dia, goal setting

DataStore

Data Management

SecurePersonal

Data Vault

Core ID, Security Services

Data alignment

Time series summarization

Summary statistics

correlations

Trend and anomaly

identificationAggregateanalysis

InfoVis

External data layers

Game and guided-in-tervention

stats

Phone us-age patterns (communi-

cations, calendars,

alarms)Current

Stacklets

Activity classification


24

Stovepipe Architecture


Patient /Caregivers Patient /Caregivers

Open mHealth Architecture


Re-usable health data and knowledge services

Standardized personal data vaults and health specific data exchange protocols

Analysis /visualization/

feedback

Processing

Storage

Data transport

Data capture

AP

PL

IC

AT

IO

NS

“Approximately 25 years ago, government and industry invested in expanded access at a crucial time in the Internet’s development. The resulting networks and ubiquity of access provided fertile ground for technologies, ideas, institutions, markets, and cultures to innovate. The payoff from this investment created a commercially viable and largely self-governing ecosystem for innovation.

The same can be done for global health. Government, commercial, and nongovernmental entities involved in health IT and innovation should cooperate to define and instantiate architecture, governance, and business models and to steer initial mHealth investments into open architecture.”

- D Estrin, I Sim. Open mHealth Architecture: An Engine for Health Care Innovation. Science Magazine, Nov, 2010.

Closing remarks (aka parting shots)

Open mHealth initiative: http://openmhealth.org

Summer reading recommendations:

The filter bubble, Eli Pariser

Everything is Obvious*, *once you know the answer, Duncan Watts


http://openmhealth.org

http://openmhealth.org

Acknowledgments: Collaborators and Sponsors

Collaborators

Technology faculty, PIs: Deborah Estrin, Mark Hansen, Nithya Ramanathan

Application/domain expert faculty/PIs (Health science): Robert Bilder, Jacqueline Casillas, Scott Comulada, Patricia Ganz, Mary Jane Rotheram-Borus, Ida Sim (UCSF), Fred Sabb, Dallas Swendeman, Michael Swiernik

Students, staff: Staff: Betta Dawson, John Jenkins, Mo Monibi, Joshua SelskyGraduate students: Faisal Alquaddoomi, Hossein Falaki, Brent Flagstaff, John Hicks, Jinha Khang, Donnie Kim, Min Mun, Katie Shilton

Sponsors and Partners/Collaborators

UCLA centers: CENS, Global center for families and children, Health Sciences, JCCC Federal funding: NSF STC and NETS-FIND Program, NIH

Corporate funding: Google, Intel, MSR, Nokia, T-Mobile Foundations/NGOs: The California Endowment, RWJF, CHCF, CRA


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